Model Instance
Simulation Parameters
Surgeries
| Surgery | Complexity | Arrival_Adjusted | Arrival_Original | Rationale |
|---|---|---|---|---|
| Surgery 1 | Complexity 1 | 1.23 | 1.0000 | once per week |
| Surgery 1 | Complexity 2 | 0.62 | 0.5000 | once per two weeks |
| Surgery 4 | Complexity 1 | 0.14 | 0.0833 | once per 3 months |
| Surgery 4 | Complexity 2 | 0.10 | 0.0625 | once per 4 months |
| Surgery 6 | Complexity 1 | 1.23 | 1.0000 | once per week |
| Surgery 6 | Complexity 2 | 0.62 | 0.5000 | once per 2 weeks |
| Surgery | Complexity | Resource_Type | Usage |
|---|---|---|---|
| Surgery 1 | Complexity 1 | Admissions | 0.0 |
| Surgery 1 | Complexity 1 | OR_Time | 3.0 |
| Surgery 1 | Complexity 2 | Admissions | 1.0 |
| Surgery 1 | Complexity 2 | OR_Time | 4.0 |
| Surgery 4 | Complexity 1 | Admissions | 1.0 |
| Surgery 4 | Complexity 1 | OR_Time | 4.0 |
| Surgery 4 | Complexity 2 | Admissions | 1.0 |
| Surgery 4 | Complexity 2 | OR_Time | 5.5 |
| Surgery 6 | Complexity 1 | Admissions | 0.0 |
| Surgery 6 | Complexity 1 | OR_Time | 1.5 |
| Surgery 6 | Complexity 2 | Admissions | 0.0 |
| Surgery 6 | Complexity 2 | OR_Time | 2.5 |
| Resouce | Capacity_Weekly | Unit |
|---|---|---|
| Admissions | 1.50 | Patients Admitted per week |
| OR_Time | 11.25 | OR Hours per week |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making.
Policy:
MDP will schedule into the first 2 days only.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 11.83 += 14.91 | 19.71 += 19.68 | 36.56 += 19.35 | 0.59 += 0.22 |
| myopic | 14.15 += 12.53 | 24.2 += 17.01 | 26.3 += 8.32 | 2.36 += 0.86 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 47.68 += 9.53 | 37.7 += 8.96 | 8.9 += 2.42 | 1.09 += 0.45 |
| myopic | 56.53 += 11.18 | 45.76 += 10.22 | 6.43 += 1.46 | 4.35 += 0.85 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.96 += 0.1 | 1.04 += 0.11 | 23.25 += 1.11 | 0.1 += 0.03 |
| myopic | 10.06 += 0.43 | 12.58 += 0.6 | 42.65 += 2.2 | 3.2 += 0.2 |
| policy | bed | OR |
|---|---|---|
| MDP | 53.51 += 12.85 | 93.48 += 7.07 |
| myopic | 64.89 += 11.87 | 95.48 += 6.82 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 3.09 += 0.09 | 4.4 += 0.19 | 5.22 += 0.6 | 1.47 += 0.1 |
| myopic | 12.82 += 0.33 | 15.3 += 0.38 | 62.53 += 2.78 | 3.8 += 0.22 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule into the first 2 days only.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.41 += 1.55 | 2.29 += 2.14 | 2.61 += 1.26 | 0.37 += 0.19 |
| myopic | 1.64 += 0.87 | 2.23 += 0.96 | 4.19 += 1.51 | 0.72 += 0.38 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 5.58 += 1.97 | 4.27 += 1.71 | 0.63 += 0.34 | 0.68 += 0.36 |
| myopic | 6.5 += 2.11 | 4.16 += 1.39 | 1.01 += 0.45 | 1.32 += 0.63 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.5 += 0.06 | 0.28 += 0.06 | 5.65 += 0.76 | 0.04 += 0.02 |
| myopic | 2.22 += 0.22 | 2.36 += 0.26 | 13.3 += 1.4 | 0.62 += 0.12 |
| policy | bed | OR |
|---|---|---|
| MDP | 57.33 += 13.38 | 86.51 += 8.1 |
| myopic | 58.69 += 10.8 | 86.85 += 7.59 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.25 += 0.07 | 1.66 += 0.13 | 3.09 += 0.44 | 0.58 += 0.08 |
| myopic | 3.93 += 0.26 | 5.48 += 0.36 | 11.91 += 1.36 | 1.31 += 0.14 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule into the first 2 days only.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.58 += 0.56 | 0.82 += 0.7 | 1.51 += 0.9 | 0.22 += 0.15 |
| myopic | 0.82 += 0.48 | 1.15 += 0.54 | 2.27 += 0.76 | 0.29 += 0.21 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 2.3 += 1 | 1.53 += 0.76 | 0.36 += 0.25 | 0.4 += 0.28 |
| myopic | 3.22 += 1.19 | 2.14 += 0.82 | 0.55 += 0.3 | 0.53 += 0.38 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.2 += 0.03 | 0.09 += 0.02 | 2.45 += 0.4 | 0.01 += 0.01 |
| myopic | 0.79 += 0.09 | 0.54 += 0.11 | 7.88 += 0.87 | 0.11 += 0.04 |
| policy | bed | OR |
|---|---|---|
| MDP | 57.32 += 14.26 | 79.23 += 8.89 |
| myopic | 57.47 += 11.04 | 79.31 += 8.21 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.51 += 0.06 | 0.7 += 0.09 | 1.39 += 0.36 | 0.19 += 0.04 |
| myopic | 1.51 += 0.14 | 2.38 += 0.22 | 3.46 += 0.57 | 0.36 += 0.07 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule into the first 2 days only.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.36 += 0.37 | 0.51 += 0.48 | 0.88 += 0.46 | 0.13 += 0.12 |
| myopic | 0.56 += 0.39 | 0.81 += 0.45 | 1.77 += 0.62 | 0.14 += 0.13 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.41 += 0.72 | 0.96 += 0.55 | 0.21 += 0.18 | 0.25 += 0.21 |
| myopic | 2.19 += 0.87 | 1.51 += 0.64 | 0.43 += 0.26 | 0.25 += 0.24 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.08 += 0.02 | 0.02 += 0.01 | 1.15 += 0.31 | 0 += 0 |
| myopic | 0.4 += 0.05 | 0.15 += 0.04 | 5.31 += 0.63 | 0.02 += 0.01 |
| policy | bed | OR |
|---|---|---|
| MDP | 57.3 += 14.98 | 73.11 += 9.21 |
| myopic | 57.28 += 11.45 | 73.13 += 8.49 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.16 += 0.03 | 0.22 += 0.05 | 0.59 += 0.27 | 0.05 += 0.02 |
| myopic | 0.53 += 0.06 | 0.85 += 0.11 | 1.49 += 0.37 | 0.09 += 0.03 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 11.87 += 8.68 | 16.33 += 5.53 | 31.65 += 27.69 | 4.74 += 3.77 |
| myopic | 22.21 += 16.42 | 16.4 += 5.38 | 75.42 += 51.36 | 21.12 += 12.27 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 47.32 += 9.29 | 30.63 += 5.47 | 7.78 += 3.7 | 8.92 += 3.2 |
| myopic | 88.6 += 20.81 | 30.81 += 6.06 | 18.66 += 7.93 | 39.14 += 9.61 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 26.33 += 0.98 | 52.45 += 1.99 | 0.07 += 0.09 | 3.27 += 0.28 |
| myopic | 31.54 += 2.63 | 53.06 += 4.56 | 9.19 += 1.67 | 12.64 += 0.89 |
| policy | bed | OR |
|---|---|---|
| MDP | 119.93 += 12.07 | 102.53 += 8.47 |
| myopic | 117.43 += 13.99 | 100.2 += 6.89 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 64.54 += 1.64 | 52.17 += 3 | 0 += NA | 85.44 += 5.78 |
| myopic | 78.62 += 4.05 | 46.86 += 7 | 0 += NA | 121.15 += 14.38 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 3.45 += 1.95 | 5.22 += 2.45 | 0.96 += 1.44 | 1.98 += 0.98 |
| myopic | 1.43 += 0.92 | 1.77 += 1.1 | 0.33 += 0.33 | 1.23 += 0.72 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 13.62 += 3.45 | 9.75 += 2.7 | 0.23 += 0.26 | 3.64 += 1.35 |
| myopic | 5.66 += 2.36 | 3.32 += 1.4 | 0.08 += 0.11 | 2.27 += 1.1 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 10.83 += 0.72 | 20.86 += 1.44 | 0.02 += 0.03 | 2.07 += 0.15 |
| myopic | 3.3 += 0.46 | 5.72 += 0.76 | 0.23 += 0.14 | 1.25 += 0.23 |
| policy | bed | OR |
|---|---|---|
| MDP | 81.2 += 19.25 | 89.23 += 11.1 |
| myopic | 62.65 += 19.18 | 87.11 += 8.43 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 35.69 += 1.28 | 46.42 += 1.61 | 0 += NA | 29.48 += 1.9 |
| myopic | 21.91 += 1.47 | 35.4 += 2.2 | 0 += NA | 11.1 += 1.09 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.69 += 1.11 | 2.1 += 1.38 | 0.28 += 0.46 | 1.45 += 0.79 |
| myopic | 0.6 += 0.41 | 0.76 += 0.5 | 0.24 += 0.24 | 0.48 += 0.29 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 6.65 += 2.72 | 3.92 += 1.82 | 0.07 += 0.12 | 2.66 += 1.21 |
| myopic | 2.37 += 1.19 | 1.42 += 0.76 | 0.06 += 0.1 | 0.88 += 0.54 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 4.14 += 0.44 | 7.43 += 0.81 | 0 += 0 | 1.33 += 0.15 |
| myopic | 0.85 += 0.12 | 1.53 += 0.22 | 0.23 += 0.18 | 0.25 += 0.07 |
| policy | bed | OR |
|---|---|---|
| MDP | 65.1 += 19.25 | 79.95 += 11.29 |
| myopic | 58.29 += 18.85 | 79.28 += 9.17 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 19.91 += 1.19 | 11.8 += 0.72 | 0 += NA | 30.78 += 2.09 |
| myopic | 9.54 += 0.68 | 16.25 += 1.14 | 0 += NA | 3.99 += 0.37 |
The policy description is based on a graph to the right
Additionally the far right graph shows the approximate decision making
Policy:
MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
In this approximate order:
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 0.75 += 0.61 | 0.91 += 0.76 | 0.14 += 0.18 | 0.66 += 0.44 |
| myopic | 0.34 += 0.26 | 0.44 += 0.31 | 0.23 += 0.23 | 0.26 += 0.19 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 2.94 += 1.63 | 1.7 += 1.06 | 0.03 += 0.07 | 1.21 += 0.77 |
| myopic | 1.35 += 0.81 | 0.83 += 0.53 | 0.06 += 0.09 | 0.47 += 0.37 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 1.56 += 0.19 | 2.72 += 0.33 | 0.02 += 0.03 | 0.57 += 0.1 |
| myopic | 0.35 += 0.07 | 0.64 += 0.13 | 0.14 += 0.09 | 0.08 += 0.03 |
| policy | bed | OR |
|---|---|---|
| MDP | 59.86 += 18.93 | 73.33 += 10.67 |
| myopic | 57.62 += 18.8 | 73.1 += 9.47 |
| policy | Overall | Surgery1 | Surgery4 | Surgery6 |
|---|---|---|---|---|
| MDP | 8.34 += 0.54 | 11.98 += 0.8 | 0 += NA | 5.74 += 0.51 |
| myopic | 4.26 += 0.43 | 7.63 += 0.77 | 0 += NA | 1.4 += 0.21 |
---
title: "Report"
date: "`r Sys.Date()`"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r setup, include=FALSE}
## Global options
library(reticulate)
library(knitr)
library(flexdashboard)
library(scales)
library(here)
library(tidyverse)
library(readr)
library(plotly)
library(tidyverse)
library(here)
knitr::opts_chunk$set(cache = TRUE)
source(here('modules','data_funcs.R'))
# PARAMS
warm <- 250
dur <- 1000
repl <- 30
path <- here('data','full-sm')
# NO CUU - OR * 1
modif <- '0-1'
dt_pl_n10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n10 <- generate_summary_zs(path, modif, FALSE)
# NO CUU - OR * 1.1
modif <- '0-1.1'
dt_pl_n11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n11 <- generate_summary_zs(path, modif, FALSE)
# NO CUU - OR * 1.2
modif <- '0-1.2'
dt_pl_n12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n12 <- generate_summary_zs(path, modif, FALSE)
# NO CUU - OR * 1.3
modif <- '0-1.3'
dt_pl_n13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n13 <- generate_summary_zs(path, modif, FALSE)
# CUU - OR * 1
modif <- '1000-1'
dt_pl_y10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y10 <- generate_summary_zs(path, modif, TRUE)
# CUU - OR * 1.1
modif <- '1000-1.1'
dt_pl_y11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y11 <- generate_summary_zs(path, modif, TRUE)
# CUU - OR * 1.2
modif <- '1000-1.2'
dt_pl_y12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y12 <- generate_summary_zs(path, modif, TRUE)
# CUU - OR * 1.3
modif <- '1000-1.3'
dt_pl_y13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y13 <- generate_summary_zs(path, modif, TRUE)
# MODEL DATA SUMMARY
arrival_rate <- data.frame(
Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 4',
'Surgery 4', 'Surgery 6', 'Surgery 6'),
Complexity = c('Complexity 1', 'Complexity 2', 'Complexity 1',
'Complexity 2', 'Complexity 1', 'Complexity 2'),
"Arrival_Adjusted" = c(1.23, 0.62, 0.14, 0.10, 1.23, 0.62),
"Arrival_Original" = c(1, 0.5, 0.0833, 0.0625, 1, 0.5),
Rationale = c("once per week", "once per two weeks", "once per 3 months",
"once per 4 months", "once per week", "once per 2 weeks")
)
resource_usage <- data.frame(
Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 1', 'Surgery 1',
'Surgery 4', 'Surgery 4', 'Surgery 4', 'Surgery 4',
'Surgery 6', 'Surgery 6', 'Surgery 6', 'Surgery 6'),
Complexity = c('Complexity 1', 'Complexity 1', 'Complexity 2',
'Complexity 2', 'Complexity 1', 'Complexity 1',
'Complexity 2', 'Complexity 2', 'Complexity 1',
'Complexity 1', 'Complexity 2', 'Complexity 2'),
Resource_Type = c('Admissions', 'OR_Time','Admissions', 'OR_Time',
'Admissions', 'OR_Time','Admissions', 'OR_Time',
'Admissions', 'OR_Time','Admissions', 'OR_Time'),
Usage = c(0,3,1,4,1,4,1,5.5,0,1.5,0,2.5)
)
resource_capacity <- data.frame(
Resouce = c('Admissions', 'OR_Time'),
Capacity_Weekly = c(1.5, 11.25),
Unit = c("Patients Admitted per week", "OR Hours per week")
)
```
Model Parameters
=======================================================================
Row
-----------------------------------------------------------------------
### Model Parameters
**Model Instance**
* Planning horizon is decreased from 24 weeks to 10 weeks
* Maximum tracked wait is decreased from 6 weeks to 4 weeks
* There are 3 surgeries instead of 6 surgeries
* Number of priorities is set to 1
**Simulation Parameters**
* 30 Replications
* 1000 weeks duration
* 250 weeks warm up
**Surgeries**
* Surgery 1 - 1. SPINE POSTERIOR DECOMPRESSION/LAMINECTOMY LUMBAR
* Surgery 4 - 4. SPINE POST CERV DECOMPRESSION AND FUSION W INSTR
* Surgery 6 - 6. SPINE POSTERIOR DISCECTOMY LUMBAR
### Arrival Rate
It was set to be 95% of the capacity, however due to transitions, the resource usage should be higher than 95%
``` {r echo=FALSE, cache=FALSE}
kable(arrival_rate)
```
Row
-----------------------------------------------------------------------
### Resource Usage
```{r echo=FALSE, cache=FALSE}
kable(resource_usage)
```
### Resource Capacity
```{r echo=FALSE, cache=FALSE}
kable(resource_capacity)
```
NP, 1
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making.
Policy:
* MDP will schedule into the first 2 days only.
* In this approximate order:
* Surgery 6, Complexity 1
* Surgery 1, Complexity 1
* Surgery 6, Complexity 2
* Surgery 4, Complexity 2
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_n10$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_n10$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$rsc_plt %>% ggplotly()
```
NP, 1.1
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule into the first 2 days only.
* In this approximate order:
* Surgery 6, Complexity 1
* Surgery 6, Complexity 2
* Surgery 1, Complexity 1
* Surgery 4, Complexity 2
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_n11$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_n11$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$rsc_plt %>% ggplotly()
```
NP, 1.2
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule into the first 2 days only.
* In this approximate order:
* Surgery 6, Complexity 1
* Surgery 6, Complexity 2
* Surgery 1, Complexity 1
* Surgery 4, Complexity 2
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_n12$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_n12$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$rsc_plt %>% ggplotly()
```
NP, 1.3
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule into the first 2 days only.
* In this approximate order:
* Surgery 6, Complexity 1
* Surgery 6, Complexity 2
* Surgery 1, Complexity 1
* Surgery 4, Complexity 2
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_n13$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_n13$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$rsc_plt %>% ggplotly()
```
P, 1
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
* Surgery 4, Complexity 2
* Surgery 6, Complexity 1
* Surgery 6, Complexity 2
* Surgery 1, Complexity 1
### Policy Math Graph
```{r echo=FALSE}
dt_zs_y10$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_y10$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$rsc_plt %>% ggplotly()
```
P, 1.1
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
* Surgery 4, Complexity 2
* Surgery 6, Complexity 1
* Surgery 1, Complexity 1
* Surgery 6, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_y11$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_y11$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$rsc_plt %>% ggplotly()
```
P, 1.2
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
* Surgery 4, Complexity 2
* Surgery 6, Complexity 1
* Surgery 1, Complexity 1
* Surgery 6, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_y12$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_y12$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$rsc_plt %>% ggplotly()
```
P, 1.3
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Policy Description
The policy description is based on a graph to the right
* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.
Additionally the far right graph shows the approximate decision making
Policy:
* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:
* Surgery 4, Complexity 1 / Surgery 1, Complexity 2
* Surgery 4, Complexity 2
* Surgery 6, Complexity 1
* Surgery 1, Complexity 1
* Surgery 6, Complexity 2
### Policy Math Graph
```{r echo=FALSE}
dt_zs_y13$zf_plt %>% ggplotly()
```
### Policy Evidence Graph
```{r echo=FALSE}
dt_sa_y13$res_plot$sched_plt %>% ggplotly()
```
Row
-----------------------------------------------------------------------
### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$pw)
```
### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$wtl)
```
### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$tr)
```
### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$util)
```
Row
-----------------------------------------------------------------------
### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$rsc)
```
### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$waitlist_plt %>% ggplotly()
```
### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$rsc_plt %>% ggplotly()
```